Semiotic Square Search And/Or Sentiment Analysis System And Method

ABSTRACT

A semiotic square search and/or sentiment analysis system and method are provided. In one implementation, a software implemented document search system and method are disclosed. The system and method may be used to analyze sentiments in various types of data including documents, blogs, text strings, posts, etc. . . .

PRIORITY CLAIM/RELATED APPLICATIONS

This application claims priority under 35 USC 119(e) and 35 USC 120 toU.S. Provisional Patent Application Ser. No. 61/179,829 filed on May 20,2009 and entitled “Semiotic Square Search and/or Sentiment AnalysisSystem and Method”, the entirety of which is incorporated herein byreference.

FIELD

The disclosure relates to a search system and method and in particularto a sentiment analysis system and method that utilizes a semioticsquare.

BACKGROUND

The “semiotic square” was initially put forward for analyzing thenarrative functions is based on works carried out at the beginning ofthe century by the Russian formalist Vladimir Prop. See for example,Propp, V. (1968). Morphology of the Folktale. Austin, University ofTexas Press. Propp drew up an inventory of the functions of the Russiantale and found an astonishing stability in their functional sequences.From one tale to another, the sequence of actions may be generalized (asshown in the list below) and brought back to a series of optionalfunctions, independent of their specific circumstances:

Initial situation (absence, prohibition, etc.)

Villainy

The hero is approached with a request or command, etc.

Departure

Test and reception of magical object

The hero and the villain join in direct combat, etc.

Liquidation of initial misfortune or lack

The hero returns, is pursued, etc.

Punishment

Marriage, etc.

Applying a structuralist approach to these formal results (Propp simplylooked for and found remarkable forms), Greimas transforms this linearsequence into a system of oppositions in Greimas, A. J. (1966),Sémantique structurale. Paris, Larousse. In the book:

-   -   He couples reciprocal functions: prohibition vs. violation,        command vs. acceptance.    -   He generalizes these pairs: (mandate vs.        acceptance)=establishment of the contract, (prohibition vs.        violation)=breaching of the contract, etc.    -   obtains a square figure: (mandate vs. acceptance) vs.        (prohibition vs. violation), in which the terms prohibition and        violation are respectively the negative (privative) forms of        command and acceptance.

An example of a semiotic square is shown in FIG. 1. This squared figureis doubly emblematic as it forms the heart of the functional outline,the “contract” and it is based on a squared figure that combines threecanonical semiotic relations:

Opposition (mandate vs. acceptance);

Absence (mandate vs. prohibition); and,

Gradation (mandate vs. violation).

“Modern logic designates the first (‘contrary’) relationship equipollent. . . ; it designates the second ('contradictory') relationshipprivative . . . : that is, the opposition formed by the presence orabsence of some quality . . . . A third logical opposition—the threetogether exhausting the logical possibilities of opposition—itdesignates arbitrary (or gradual) . . . : that is, the opposition formedby cultural (and hence ‘arbitrary’) categories . . . . The elements ofthis last opposition often appear on a continuum . . . : hence thedesignation gradual” as described in Schleifer, R. (1983). Introduction.Structural semantics. Lincoln, University of Nebraska Press: xii-lvi,pg. xxxiii. Greimas can therefore resume the dynamics of the narrativefunctions in a functional outline built around a double inversion asshown in FIG. 2.

It is desirable to utilize the semiotic square to perform sentimentanalysis and searches which is not performed by current systems andmethod. Thus, it is desirable to provide a semiotic square sentimentanalysis system and method and it is to this end that the disclosure isdirected.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a semiotic square;

FIG. 2 illustrates the sequence of actions in the square;

FIG. 3 illustrates a web-based implementation of a search system and/orsentiment analysis system implemented using a semiotic square library;

FIG. 4 illustrates a semiotic square model that can be used in a searchsystem or a sentimental analysis system;

FIG. 5 illustrates a method for generating the semiotic square modelthat can be used in a search system or a sentimental analysis system;

FIG. 6 illustrates examples of semiotic square for a desire property;

FIG. 7 illustrates examples of semiotic square for a deontic property;

FIG. 8 illustrates examples of semiotic square for a trust property;

FIG. 9 illustrates examples of semiotic square for an ability property;

FIG. 10 illustrates examples of semiotic square for a knowledgeproperty;

FIG. 11 illustrates examples of semiotic square for a power property;

FIG. 12 illustrates examples of semiotic square for an aestheticsproperty;

FIGS. 13A and 13B illustrate examples of semiotic square for an ethicsproperty;

FIG. 14 illustrates examples of semiotic square for a thymic property;

FIGS. 15A-15C illustrate examples of semiotic square for a postureproperty;

FIG. 16 illustrates examples of semiotic square for a self/othersproperty;

FIG. 17 illustrates a generic thesaurus data structure for a thesaurusimplementation of the semiotic squares library;

FIG. 18 illustrates an example of the thesaurus for a particularsemiotic square;

FIG. 19 illustrates an example of the syntax for semiotic markers thatcan be used in the thesaurus implementation of the semiotic squareslibrary;

FIG. 20 illustrates an example of the syntax for intensity marker thatcan be used in the thesaurus implementation of the semiotic squareslibrary;

FIG. 21 illustrates an example of the semiotic markers and intensitymarkers for a semiotic square;

FIG. 22 illustrates an example of the syntax for idiomatic phrases thatcan be used in the indexing implementation of the semiotic squareslibrary;

FIG. 23A-23B illustrates an example of the syntactic analysis performedusing the indexing implementation of the semiotic squares library;

FIG. 24 illustrates an example of the categorization that is part of theindexing implementation of the semiotic squares library;

FIG. 25 illustrates a sentiment grid of the indexing implementation ofthe semiotic squares library;

FIGS. 26 and 27 illustrate an example of a search using the semioticsystem;

FIG. 28 illustrates an example of a sentiment analytics using thesemiotic system; and

FIG. 29 illustrates another example of a sentiment analytics using thesemiotic system.

DETAILED DESCRIPTION OF ONE OR MORE EMBODIMENTS

The disclosure is particularly applicable to a software implementeddocument search system and method and it is in this context that thedisclosure will be described. It will be appreciated, however, that thesystem and method has greater utility since the system and method can beimplemented using hardware or a combination of hardware and software andthe system and method may be used to analyze sentiments in various typesof data including documents, blogs, text strings, posts, etc. . . . andthe system and method are not limited to the particular implementationdescribed below.

FIG. 3 illustrates a web-based implementation of a search system and/orsentiment analysis system 30 implemented using a semiotic squarelibrary. The system may include one or more devices 32 (such as devices32 a, 32 b, . . . , 32 n as shown in FIG. 3) that allow a user toconnect to and interact with, over a link 34, a search and analysissystem 36. Each device 32 may be a processing unit based device withsufficient processing power, memory and connectivity capabilities to beable to connect over the link to the search and analysis system 36. Forexample, each device 32 may be a personal computer, laptop computer, amobile phone, a smart phone or the like. The link 34 may be a wired orwireless link, such as a communications network or computer network,wherein the link may be, for example, Ethernet, LAN, WLAN, WIFI, acellular network, a digital data network (EDGE and the like), etc. . . .. The search and analysis system 36 may be implemented in oneembodiment, as one or more typical server computers executing computercode that implement the various functions and operations of the searchand analysis system 36 as described below. However, various elements ofthe search and analysis system 36 may also be implemented in hardware.In general, the search and analysis system 36 allows a user to access itvia the link and perform searches or sentiment analysis using thesemiotic square library that is described in more detail below.

The search and analysis system 36 may further comprise a known webserver 40 (that may be implemented using a plurality of lines ofcomputer code) that interacts with the devices 32 and serves web pagesto those devices wherein the devices 32 may further comprise an softwareapplication, such as a browser, that allows the device 32 to establish aconnection with the web server 40 and exchange data/information with theweb server 40 such as web pages, forms to be filled in with data andresults of an action requested by the user of the device such as searchresults and/or a sentiment analysis. The search and analysis system 36may further comprise a semiotic square store 42 that may be implementedin software or hardware and stores a plurality of semiotic squareswherein the semiotic squares are described below in more detail. Theplurality of semiotic squares allow the search and analysis system 36 toperform searches and sentiment analysis using the semiotic squares asdescribed below in more detail. The search and analysis system 36 mayalso have a semiotic square generator unit 43 that generates, asdescribed below, the semiotic squares that are stored in the semioticsquare store 42.

The search and analysis system 36 may further comprise a search engine44 (implemented as a plurality of lines of computer code in oneembodiment although it can also be implemented in hardware) thatreceives a search request from a device 32 via the web server 40,performs a search in part based on the plurality of semiotic squares asdescribed below in the corpus 38 and returns search results to thedevice 32, such as by having the web server 40 deliver a web page to thedevice although the search results can be delivered to the device 32 ina different manner. The search and analysis system 36 may furthercomprise a sentiment analysis engine 46 (implemented as a plurality oflines of computer code in one embodiment although it can also beimplemented in hardware) that receives a sentiment analysis request froma device 32 via the web server 40, performs a sentiment analysis in partbased on the plurality of semiotic squares as described below andreturns the sentiment analysis results to the device 32, such as byhaving the web server 40 deliver a web page to the device although thesearch results can be delivered to the device 32 in a different manner.The corpus 38 may be a collection of data (documents, web pages, videos,etc.) that may be searched using the search engine 44 as describedabove. In addition, the corpus 38 may also be used in part to generatethe semiotic squares using the semiotic square generator unit 43. Now,the semiotic square library generation method and model is described inmore detail.

FIG. 4 illustrates a semiotic square model 50 that can be used in asearch system or a sentimental analysis system. Unlike the typicalsemiotic square discussed above, the semiotic square model 50 is a modelthat allows a library of semiotic squares to be generated using themodel. The first relationship is based on a diagonal contradiction 52which is the presence/absence of a particular property such asIntelligence, Wealth, Appetite, Power, Beauty, Honesty, etc. As shown inFIG. 4, 2 pairs of diagonal contradictions 52 are obtained that arecalled A/Non-A, and B/Non-B as shown in FIG. 4. The second relationshipis an opposition or antagonism of aims. For example, if appetite isaimed at eating, then anorexia is aimed at fasting. This is not aprivative relationship, as anorexia is not defined by an absence ofappetite, but instead by a willingness to not eat. In the model 50,A/Non-A is the contradiction between do and don't do and A/B is theopposition between do and do not. Far from being privative, the will isequipollent, but the outcome is oriented in an opposite direction.

In the semiotic square model 50, “A” is the A-ness which is the fullrealization of the property, the no nonsense summit, the factual one and“Non-A” is the failure, the absence, the un-A. “B” is the de-A, where Ais deconstructed; this is a darker summit, where renouncement goes withdeceit (renouncement to trust) or denial (renouncement to assertion).Finally, “Non-B” is the privation of this negative orientation, turningthe same energy into an additive relationship.

The semiotic square model 50 may also have two Deixis 54 including aDeixis positive 54 a and a Deixis negative 54 b. The 2 “Deixis” areperspectives are pulled into antagonist directions by the 2 inversionsof privative/additive and assertion/renouncement, resulting into 2positive/negative sets. However the 2 Deixis are not exactlysymmetrical. The Deixis positive 54 a set of A/Non-B is roughly based ona gradation of degrees or presupposition of states with excess directlypresupposes assertion and is a more intense state of the same. This isnot true for the Deixis negative 54 b in which denial (B) does notexactly suppose failure.

In order to respect the nuances of intensity attached to the model, themodel may include two levels into each summit value (major, minor)resulting into 4 grades for each diagonal, e.g. Major assertion, minorassertion, minor failure, major failure. Now, the method for generatingsemiotic squares using the model is described in more detail.

FIG. 5 illustrates a method 60 for generating the semiotic square model50 that can be used in a search system or a sentimental analysis system.In a first process, the method starts at “A” by determining the fact tofocus on that is positive and is most often better expressed as acombination of an auxiliary and a property such as, for example, “wantto eat”, “can learn”, “is beautiful”, but not “refrain from smoking.” Ina second process 61, the non-A is generated by removing the propertysuch as, for example, “doesn't want to eat=disgust, nausea” for“A”=“want to eat”, “cannot learn=uneducated” for “A”=“can learn” or “isnot beautiful=ugly” for “A”=“is beautiful.” In a third process 62, “B”is generated which gets back to A keeping the same willingness, butrevert the direction such as, for example, “wants to eat (not)=anorexia”for “A”=want to eat”, “can learn (not)=illiterate” for “A”=“can learn”or “is beautiful (not)=neglected” for “A”=“is beautiful.” In a fourthprocess 63, the non-B is generated in which strength is added into theopposite direction such as, for example, “wants to eat (all)=gluttony”for “B”=“wants to eat (not)=anorexia”, “can learn (all)=savant” for“B”=“can learn (not)=illiterate” or “is beautiful (all)=charming” for“B”=“is beautiful (not)=neglected”.

Using the model and method shown in FIGS. 4 and 5, a library of semioticsquares used by the system and method shown in FIG. 3 may be generated.The complete set of semiotic squares may be generated by changing themodes of assertion: Obligation, Ability, etc. The shift of modes can usethe method in that, from one series to the next, the system generatesthe same balance of assertions and denials, resulting in a transmodalmeta-model of facts, rebuttals, denials and excesses, such as forexample, shown in the Table A below:

A Non-A B Non-B Desire Appetite Nausea Anorexia Gluttony ConfidenceTrust Distrust Deceit Loyalty Competence Skill Incompetence CarelessScrupulous Pleasure Joy Sorrow Contrition Ecstasy Attitude AssertiveServile Denial Flamboyant

The following dimensions organize the library of “Sentiments”:

1. Modal fundamentals

-   -   a. Power (will)    -   b. Desire (want)    -   c. Morals        -   1. Deontic, i.e. Obligation, Duty (must)        -   2. Contract (trust)    -   d. Ability, Knowledge (can, know)

2. Axiology

-   -   a. Aesthetics    -   b. Ethics

3. Thymic

-   -   a. Emotions

FIG. 6 illustrates examples of semiotic square for a desire property,such as for example, a desire semiotic square having the “A”, “non-A”,“B” and “non-B” values shown, a money semiotic square having the “A”,“non-A”, “B” and “non-B” values shown, a food semiotic square having the“A”, “non-A”, “B” and “non-B” values shown and a sex semiotic squarehaving the “A”, “non-A”, “B” and “non-B” values shown. Similarly, FIG. 7illustrates examples of semiotic square for a deontic property, such asfor example, a duty semiotic square having the “A”, “non-A”, “B” and“non-B” values shown, a permission semiotic square having the “A”,“non-A”, “B” and “non-B” values shown and a discipline semiotic squarehaving the “A”, “non-A”, “B” and “non-B” values shown. FIG. 8illustrates examples of semiotic square for a trust property, such asfor example, a confidence semiotic square having the “A”, “non-A”, “B”and “non-B” values shown, an accuracy semiotic square having the “A”,“non-A”, “B” and “non-B” values shown, a contract semiotic square havingthe “A”, “non-A”, “B” and “non-B” values shown and a pertinent semioticsquare having the “A”, “non-A”, “B” and “non-B” values shown. FIG. 9illustrates examples of semiotic square for an ability property, such asfor example, a competence semiotic square having the “A”, “non-A”, “B”and “non-B” values shown and a disposition semiotic square having the“A”, “non-A”, “B” and “non-B” values shown.

FIG. 10 illustrates examples of semiotic square for a knowledgeproperty, such as for example, a intelligence semiotic square having the“A”, “non-A”, “B” and “non-B” values shown, an education semiotic squarehaving the “A”, “non-A”, “B” and “non-B” values shown, a cognitionsemiotic square having the “A”, “non-A”, “B” and “non-B” values shownand a pertinent semiotic square having the “A”, “non-A”, “B” and “non-B”values shown, a clarity semiotic square having the “A”, “non-A”, “B” and“non-B” values shown and a pertinent semiotic square having the “A”,“non-A”, “B” and “non-B” values shown, a cognizance semiotic squarehaving the “A”, “non-A”, “B” and “non-B” values shown and a pertinentsemiotic square having the “A”, “non-A”, “B” and “non-B” values shown,an experience semiotic square having the “A”, “non-A”, “B” and “non-B”values shown and a pertinent semiotic square having the “A”, “non-A”,“B” and “non-B” values shown and an awareness semiotic square having the“A”, “non-A”, “B” and “non-B” values shown and a pertinent semioticsquare having the “A”, “non-A”, “B” and “non-B” values shown. FIG. 11illustrates examples of semiotic square for a power property, such asfor example, a capture semiotic square having the “A”, “non-A”, “B” and“non-B” values shown, a coercion semiotic square having the “A”,“non-A”, “B” and “non-B” values shown, an order semiotic square havingthe “A”, “non-A”, “B” and “non-B” values shown, a conflict semioticsquare having the “A”, “non-A”, “B” and “non-B” values shown, aresponsibility semiotic square having the “A”, “non-A”, “B” and “non-B”values shown and a retribution semiotic square having the “A”, “non-A”,“B” and “non-B” values shown.

FIG. 12 illustrates examples of semiotic square for an aestheticsproperty, such as for example, a pleasure semiotic square having the“A”, “non-A”, “B” and “non-B” values shown, a value semiotic squarehaving the “A”, “non-A”, “B” and “non-B” values shown, a taste semioticsquare having the “A”, “non-A”, “B” and “non-B” values shown and abeauty semiotic square having the “A”, “non-A”, “B” and “non-B” valuesshown. FIGS. 13A and 13B illustrate examples of semiotic square for anethics property, such as for example, a conscience semiotic squarehaving the “A”, “non-A”, “B” and “non-B” values shown, a qualitysemiotic square having the “A”, “non-A”, “B” and “non-B” values shown, atemperance semiotic square having the “A”, “non-A”, “B” and “non-B”values shown, a safety semiotic square having the “A”, “non-A”, “B” and“non-B” values shown, an earnestness semiotic square having the “A”,“non-A”, “B” and “non-B” values shown, an accessibility semiotic squarehaving the “A”, “non-A”, “B” and “non-B” values shown, a truth semioticsquare having the “A”, “non-A”, “B” and “non-B” values shown, a goodsemiotic square having the “A”, “non-A”, “B” and “non-B” values shown, aconfession semiotic square having the “A”, “non-A”, “B” and “non-B”values shown, an equity semiotic square having the “A”, “non-A”, “B” and“non-B” values shown, a respect semiotic square having the “A”, “non-A”,“B” and “non-B” values shown and a help semiotic square having the “A”,“non-A”, “B” and “non-B” values shown.

FIG. 14 illustrates examples of semiotic square for a thymic property,such as for example, a love semiotic square having the “A”, “non-A”, “B”and “non-B” values shown, a compassion semiotic square having the “A”,“non-A”, “B” and “non-B” values shown, an empathy semiotic square havingthe “A”, “non-A”, “B” and “non-B” values shown, a hear semiotic squarehaving the “A”, “non-A”, “B” and “non-B” values shown and a generositysemiotic square having the “A”, “non-A”, “B” and “non-B” values shown.FIGS. 15A-15C illustrate examples of semiotic square for a postureproperty, such as for example, an attitude semiotic square having the“A”, “non-A”, “B” and “non-B” values shown, a tangible semiotic squarehaving the “A”, “non-A”, “B” and “non-B” values shown, an ontologysemiotic square having the “A”, “non-A”, “B” and “non-B” values shown, avanity semiotic square having the “A”, “non-A”, “B” and “non-B” valuesshown, a composure semiotic square having the “A”, “non-A”, “B” and“non-B” values shown, a drive semiotic square having the “A”, “non-A”,“B” and “non-B” values shown, a visible semiotic square having the “A”,“non-A”, “B” and “non-B” values shown, an aletheia semiotic squarehaving the “A”, “non-A”, “B” and “non-B” values shown, an accomplishmentsemiotic square having the “A”, “non-A”, “B” and “non-B” values shown,an assertion semiotic square having the “A”, “non-A”, “B” and “non-B”values shown, an open semiotic square having the “A”, “non-A”, “B” and“non-B” values shown, a fortune semiotic square having the “A”, “non-A”,“B” and “non-B” values shown, a fact semiotic square having the “A”,“non-A”, “B” and “non-B” values shown, and a consistency semiotic squarehaving the “A”, “non-A”, “B” and “non-B” values shown. FIG. 16illustrates examples of semiotic square for a self/others property, suchas for example, a communication semiotic square having the “A”, “non-A”,“B” and “non-B” values shown and a dependency semiotic square having the“A”, “non-A”, “B” and “non-B” values shown.

All of the examples of semiotic squares for different properties can becombined together to form the semiotic square library. The model andlibrary of semiotic squares and the storage of the library may beimplemented in several different manners. In particular, the library maybe stored in a hardware device or software store. The library may begenerated and stored in the form of a thesaurus or may be generated andstored using indexing. In addition, the library of semiotic squares maybe implemented using other methods/systems that are capable ofgenerating and storing the library of semiotic squares. Now, twoexamples of implementations of the semiotic square library are describedin more detail.

Thesaurus Implementation

In one implementation, the library of semiotic squares may be stored ina thesaurus data structure in which the generic thesaurus data structuremay be as shown in FIG. 17 and an example of the thesaurus for aparticular semiotic square is shown in FIG. 18. The thesaurus datastructure may include semiotic square labels including semiotic markers(an example of which is shown in FIG. 19) where the above generic formatmay be modified to include semiotic prefixes: SP, combined with 4semiotic positions: A, Non-A, B, Non-B as shown in FIG. 19. The genericthesaurus format also may be modified to include semiotic intensity asshown in FIG. 20: SI, combined with 2 levels: High, Mild (Neutral is thecenter of the square) as shown in FIG. 20. Thus, a portion of thethesaurus for a particular semiotic square may be stored in thethesaurus as shown in FIG. 21.

Indexing Implementation

The indexing implementation may include the processes of tokenization,syntactic analysis, categorization, sentiment/concept building andsentiment grids as described below.

Tokenization

The tokenization process breaks the input text in tokens: keywords,separators and punctuation. The tokenizer then reduces keywords to theirstem, expands contraction forms (possessive, auxiliary, negative forms,etc.), and detects idioms. All of the tokens may also be tagged withpart-of-speech markers.

Using morphology analysis and stemming, the tokenizer, in addition tothe generic stemming of plurals, reduces nouns, adverbs and adjectivesto their core form if necessary in order to reduce the length of thethesaurus lists of synonyms to a manageable size. For example, thestemming may be:

Greediness->greedy->greed

Economically->economic

Infectious->infect

However, this reduction is language dependent:Prochainement->prochaine->prochain

The tokenizer may detect idiomatic phases and the semiotic thesaurus mayincludes many idioms, which have to be handled with specific care (vs.keyword entries which can be directly paired with tokens) as shown inFIG. 22.

Syntactic Analysis

The syntactic parser applies specific rules on top of the tokenizer, andbuilds a hierarchical representation of the syntax which isolates thequalificatory components and prepares for the semiotic analysis of thesentiments typically expressed by adverbs and adjectives. Note that thesyntactic parser is multi-lingual. In the example shown in FIGS.23A-23B, a sentence is analyzed against a basic SVO parsing structureare part-of-speech are associated with syntactic components(“PRF”=Preposition), qualifying components are pulled out from theirphrasal context (“cosmetic”), idioms are recognized (“dior homme dermosystem”), and semiotic markers are associated with some qualifiers(“care” is A, High, and Deixis is Positive).

Categorization

During the categorization process of the indexing implementation, thenoun phrases located in the parsing tree are matched with the relevantthesauri: vertical content (i.e. cosmetics) and semiotic thesaurus. Theresult is a hierarchy of categories coming from the thesauri, on top ofconcepts extracted from the noun phrases. In the example shown in FIG.24, Desire and Compassion refer to semiotic squares, while the other topcategories refer to Properties (Style), Health (Skin Disorders) orCosmetics (Brands).

Sentiment/Concept Binding

The final step of the semiotic implementation “binds” the sentiments tocategories. In one implementation, the binding may be done by pairingthe qualifying constituents of the parsing tree with the “nouns” (nounsor idioms standing for nouns) which they qualify. The binding patternsmay include:

Noun+Qualifier (Adjective or Adverb).

Other patterns cross the boundaries of the noun phrase:

-   -   Noun+Auxiliary+Qualifier    -   Noun+Preposition+Qualifier    -   Qualifier+Preposition+Noun

etc.

Some short adverbs (“very”, “more”, “much”, etc.) add a level ofintensity to qualifiers and they are processed accordingly to adjust thesemiotic intensity of the qualifier.

In addition, true negation (“not”), in plain or contracted form, is usedto revert the semiotic value of the qualifier: “not happy”=“unhappy”.

Sentiments Grid

The result of the semiotic analysis is a grid binding categories withtheir sentiment value as shown in FIG. 25. Given any specific category,on the one hand this category is bound to one or several thesauri,vertical or semiotic, and this can be expressed by lineages of broaderterms; on the other hand this category is also bound to a qualifyingcontext which has specific semiotic values: semiotic square position andintensity. The combination of these two sets of references allows amultiplicity of perspectives, intersecting with classes of positive andnegative Deixis.

Now, several examples of how the semiotic analysis and semiotic squarelibrary may be used is described in more detail including a searchexample and a sentiment analytics example.

Search Example

The semiotic thesauri can be used to expand a query to all its narrowerconstituents. This is true for vertical categories, like “SkinDisorders” in vertical Health, highlighting “wrinkles” and “eczema” asshown in the example in FIG. 26.

This is also true for semiotic categories, like “Desire”: As shown inFIG. 27, the highlighted “outrageous” excess of desire, attached in thiscontext to “the ‘sexy’ image of your creams”. This shows the power ofsquare representation of sentiments, versus conventional scales.

In both FIGS. 26 and 27, a left hand side 100 of the user interfacelists one or more semiotic facets that have been collected as a resultof the query that come from various thesauri. In the previous example inFIG. 27, “Pulsions”, “Posture”, “Morale” and “Power” come from thesemiotic thesaurus.

Sentiment Analytics Example

FIG. 28 illustrates an example of a sentiment analytics using thesemiotic system using semiotic widgets. The example of FIG. 28 showsthat narrower and broader categories 110 (blue left) can be sorted toshow their qualifying context 112 on the right. The sets of semioticvalues (green=positive and red=negative, shades of green and red expressintensity) can also be sorted to consider all the positives andnegatives at once.

Additional representations allow to leverage further the sophisticationof the semiotic model. For instance a square graphical representationallows the mapping at once all the semiotic positions of the qualifiersand their context.

The example of FIG. 29 shows that a square graphical representation canbe used to represent the four semiotic positions defined in FIG. 3: A(Fact), Non-A (Rebuttal), B (Denial), and Non-B (Excess). The categories(“Hair care”, Skincare”, “Price”, “Make-up”) are positioned on the foursummits of the square according to their semiotic values (green=positiveand red=negative, shades of green and red express intensity).

While the foregoing has been with reference to a particular embodimentof the invention, it will be appreciated by those skilled in the artthat changes in this embodiment may be made without departing from theprinciples and spirit of the invention, the scope of which is defined bythe appended claims.

1. A semiotic square analysis system, comprising: a storage devicestoring a plurality of semiotic squares, each semiotic square furthercomprising a fact, a rebuttal of the fact located diagonally opposite ofthe fact, a denial located horizontally opposite of the fact and anexcess located diagonally opposite of the denial wherein each semioticsquare defines a sentiment; and a computer system that utilizes theplurality of semiotic squares to analyze a request, wherein the computersystem further comprises a search engine that utilizes one or more ofthe plurality of semiotic squares to analyze a search request based on acorpus of data.
 2. The system of claim 1, wherein the computer systemfurther comprises a semiotic square generator that generates theplurality of semiotic squares stored in the storage device.
 3. Thesystem of claim 2, wherein the semiotic square generator determines apositive fact that is a combination of an auxiliary and a property,generates a non-positive fact by removing the property from the positivefact, generates a negative fact that is a combination of a negativeauxiliary and the property and generates a more negative fact that is acombination of a more negative auxiliary and the property.
 4. The systemof claim 1, wherein the computer system further comprises a sentimentanalysis engine that performs a sentiment analysis of the search requestbased on the plurality of semiotic squares and returns a sentimentanalysis results.
 5. The system of claim 1, wherein each semiotic squareis stored in a thesaurus data structure.
 6. The system of claim 1,wherein each semiotic square is indexed.
 7. The system of claim 1,wherein the corpus of data further comprises one or more of a document,a blog, a text string, a post and a video.
 8. The system of claim 1further comprising one or more computing devices that are capable ofbeing coupled to the computer system over a link, wherein each computingdevice generates a search request and communicates the search request tothe computer system.
 9. The system of claim 8, wherein each computingdevice further comprises one of a personal computer, a laptop computer,a mobile phone and a smart phone.
 10. The system of claim 8, wherein thelink further comprises one of a wired link and a wireless link.
 11. Thesystem of claim 8, wherein the computer system further comprises one ormore server computers.
 12. A semiotic square analysis system,comprising: a storage device storing a plurality of semiotic squares,each semiotic square further comprising a Deixis positive and a Deixisnegative; and a computer system that utilizes the plurality of semioticsquares to analyze a request, wherein the computer system furthercomprises a search engine that utilizes one or more of the plurality ofsemiotic squares to analyze a search request based on a corpus of data.13. A method for generating a semiotic square model, the methodcomprising: determining, using a computer implemented semiotic squaregenerator, a positive fact that is a combination of an auxiliary and aproperty; generating, using the computer implemented semiotic squaregenerator, a non-positive fact by removing the property from thepositive fact; generating, using the computer implemented semioticsquare generator, a negative fact that is a combination of a negativeauxiliary and the property; and generating, using the computerimplemented semiotic square generator, a more negative fact that is acombination of a more negative auxiliary and the property.